Day-ahead optimal bidding and scheduling strategies for DER aggregator considering responsive uncertainty under real-time pricing

Wang, Fei; Ge, Xinxin; Yang, Pingxiong; Li, Kangping; Mi, Zengqiang; Siano, Pierluigi; Duić, Neven

doi:10.1016/j.energy.2020.118765

Cited by 102 publications

(20 citation statements)

References 41 publications

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“…Many studies have proposed optimal bidding methodologies for a DERA considering the uncertainties of DRESs. In [21], Wang et al proposed a price-responsive optimal bidding method in the real-time market, considering the uncertainty of wind turbines and photovoltaics. In [22], the gap decision theory was applied for the optimal bidding of a DERA to address uncertainties.…”

Section: A Literature Focusing On the Dera's Optimal Bidding Considering Uncertaintiesmentioning

confidence: 99%

Prequalification Scheme of a Distribution System Operator for Supporting Wholesale Market Participation of a Distributed Energy Resource Aggregator

et al. 2021

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Increasing penetration of distributed renewable energy sources (DRESs) has resulted in the emergence of distributed energy resource aggregators (DERAs). A DERA participates in the transmissionlevel market operated by a transmission system operator (TSO), and the DERA's resources are connected to a jurisdiction of the distribution system operator (DSO). Inspired by the structure of the Korean power industry, this study assumes a minimal DSO that cannot directly dispatch the resources in its system. In this study, we develop a detailed procedure for prequalification wherein the DSO checks the DERA's bids that are submitted to the TSO markets. The proposed prequalification enables the DSO to secure the reliability of its system by providing limited network information to the DERA. The DERA modifies its bid until potential overvoltage and overflow problems are resolved, even in the worst case, including uncertainties. The proposed prequalification process is verified using the IEEE 33-bus distribution network. Compared to previous studies, the results demonstrate that the proposed prequalification can deal with distribution system constraints, even though uncertainties are included. The proposed prequalification process can be applied to power industries where the DSO does not have the full dispatch authority on DRESs.

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Section: A Literature Focusing On the Dera's Optimal Bidding Considering Uncertaintiesmentioning

confidence: 99%

Prequalification Scheme of a Distribution System Operator for Supporting Wholesale Market Participation of a Distributed Energy Resource Aggregator

et al. 2021

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“…Whereas in hour-ahead DR, consumers can only reschedule the price of the next hour, where they only have information on the current price and hour ahead price, hence it is unlikely that they can reschedule their demands ahead of time, making all cross-elasticities above the diagonal zero [23]. Some researchers considered all off-diagonals to be zero in this case [24].…”

Section: Consumer Behavior Modelingmentioning

confidence: 99%

“…Qu et al proposed an improvement to the price elasticity matrix of demand (PEMD) to unify the modeling of rigid and flexible loads by introducing a weighting factor and measured the effect of price policies and load types on calculating the elasticity matrix [23]. Wang et al implemented an optimal strategy for both bidding and scheduling for aggregators of distributed energy resources (DER) who manage distributed solar and wind energies and battery storage systems under uncertain consumer response to real-time pricing (RTP) considering uncertainties of renewable energy generation and consumer response [24]. Hlalele et al presented an optimization model that considers the combination of direct load control demand response and economic dispatch under renewable obligation policies, where the model maintains a predefined renewable energy share in the mix of different energy sources [25].…”

Section: Introduction 1backgroundmentioning

confidence: 99%

Day-Ahead Residential Electricity Demand Response Model Based on Deep Neural Networks for Peak Demand Reduction in the Jordanian Power Sector

2021

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In this paper, a comprehensive demand response model for the residential sector in the Jordanian electricity market is introduced, considering the interaction between the power generators (PGs), grid operators (GOs), and service providers (SPs). An accurate day-ahead hourly short-term load forecasting is conducted, using deep neural networks (DNNs) trained on four-year data collected from the National Electric Power Company (NEPCO) in Jordan. The customer behavior is modeled by developing a precise price elasticity matrix of demand (PEMD) based on recent research on the short-term price elasticity of Jordan’s residential and the analysis of the different types of electrical appliances and their daily operational hours according to the latest surveys. First, the DNNs are fine-tuned with a detailed feature analysis to predict the day-ahead hourly electrical demand and achieved a mean absolute percentage error (MAPE) of 1.365% and 1.411% on the validation and test datasets receptively. Then the predictions are used as input to a detailed model of the Jordanian power grid market, where a day-ahead peak-time demand response policy for the residential sector is applied to the three distribution power companies in Jordan. Based on different PEMD analyses for the Jordanian residential sector, the results suggest a reduction potential of 5.4% in peak demand accompanied by a cost reduction of USD 154,505 per day for the Jordanian power sector.

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“…Stochastic optimization requires the probability distributions of uncertain parameters along with numerous scenarios to address uncertainties and yield a guaranteed solution 11 . However, the probabilistic distribution of some uncertain parameters is challenging to be acquired 12 . Accordingly, the stochastic model of the MG operation will be complicated and needs to improve its computational burden.…”

Section: Introductionmentioning

confidence: 99%

Robust PSO ‐based framework for concurrent active/reactive and reserve management in microgrids in the presence of energy storage systems

2021

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Summary Nowadays, concurrent participation of distributed energy resources in providing electrical energy and ancillary services brings many benefits. Also, energy storage systems (ESSs) along with renewable generations have a growing penetration rate in modern power systems aimed at declining environmental issues. Due to the uncertainties of intermittent nonpredictable power resources in microgrids (MGs), high integration of these products leads to an increase in ancillary services requirements and it necessitates the coordinated management of these technologies with the ESSs. In this paper, for the first time, a robust model based on particle swarm optimization (PSO) metaheuristic is developed to cope with the uncertainty of renewables in concurrent active/reactive and reserve management problem in the MG with ESS. The robust framework provides a medium priority in comparison with deterministic and stochastic techniques. The objective function of the robust concurrent active/reactive and reserve scheduling problem in MGs is expressed as maximizing social welfare (SW). The proposed model is carried out using a max‐min optimization scheme. The robust design will be attained in such a way that the maximizer at the outer level seeks an optimal solution against the worst‐case objective function achieved through the minimizer at the inner level taking into account the uncertainty neighborhood. The performance of the presented approach has been evaluated on a typical MG. Simulation results verify that the suggested robust‐PSO technique will aid MG operators to decrease daily operational costs and to yield a higher SW.

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Day-ahead optimal bidding and scheduling strategies for DER aggregator considering responsive uncertainty under real-time pricing

Cited by 102 publications

References 41 publications

Prequalification Scheme of a Distribution System Operator for Supporting Wholesale Market Participation of a Distributed Energy Resource Aggregator

Prequalification Scheme of a Distribution System Operator for Supporting Wholesale Market Participation of a Distributed Energy Resource Aggregator

Day-Ahead Residential Electricity Demand Response Model Based on Deep Neural Networks for Peak Demand Reduction in the Jordanian Power Sector

Robust PSO ‐based framework for concurrent active/reactive and reserve management in microgrids in the presence of energy storage systems

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